Real-Time Instance Segmentation and Tip Detection for Neuroendoscopic Surgical Instruments

Location information of surgical instruments and their tips can be valuable for computer-assisted surgical systems and robotic endoscope control systems. While real-time methods for instrument segmentation and tip detection have been proposed for minimally invasive abdominal surgeries, the challenges become even greater in minimally invasive neurosurgery due to its narrow operating space and diverse tissue characteristics. In this paper, we introduce a real-time approach for instance segmentation and tip detection of neuroendoscopic surgical instruments. To address the specific requirements of neurosurgery, we design a tailored data augmentation strategy for this field and propose a mask filtering module to eliminate false-positive masks. Our method is evaluated using both a neurosurgical dataset and the EndoVis15’ dataset. The experimental results demonstrate that the data augmentation module improves the accuracy of instrument detection and segmentation by up to 12.6%. Moreover, the mask filtering module enhances the precision of instrument tip detection with an improvement of up to 39.51%.